Edinburgh Research Explorer

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Related Edinburgh Organisations

Open Access permissions

Open

Documents

https://ieeexplore.ieee.org/document/7140009
Original languageEnglish
Title of host publicationIEEE Intl. Conf. on Robotics and Automation (ICRA 2015)
Place of PublicationSeattle, WA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5783-5790
Number of pages8
ISBN (Electronic)978-1-4799-6923-4
DOIs
StatePublished - 2 Jul 2015
Event2015 IEEE International Conference on Robotics and Automation - Seattle, United States
Duration: 26 May 201530 May 2015
http://icra2015.org/

Conference

Conference2015 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2015
CountryUnited States
CitySeattle
Period26/05/1530/05/15
Internet address

Abstract

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPUaccelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.

Event

2015 IEEE International Conference on Robotics and Automation

26/05/1530/05/15

Seattle, United States

Event: Conference

Download statistics

No data available

ID: 19942368